Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/9/26 has been entered.
In amendments dated 3/9/26, Applicant amended claims 1-4, 12-14, 16-18, and 20, canceled no claims, and added no new claims. Claims 1-20 are presented for examination.
Rejections under 35 U.S.C. 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1, 1,3 and 17 each recites automatically providing the prompt to an artificial intelligence process; automatically generating, by the artificial intelligence process, an analysis of the prompt, wherein the analysis includes automatically determining a threshold value based on user relevant data corresponding to a dataset associated with the user, wherein the user relevant data includes tracking data identifying one or more data associated with the user including contextually relevant information associated with one or more external factors used to generate a predictive data set; automatically generating, in real-time, the predictive data set based on the analysis of the prompt, the one or more external factors, and the subset of data associated with the user, wherein generating the predictive data set further includes analyzing the tracking data utilizing the contextually relevant information, wherein the one or more external factors are determined based on a plurality of information received from one or more sensors and the dataset associated with the user, wherein performing the analysis further includes identifying an output including an identified type of the predictive data set based on the prompt, an identified metric related to the prompt, an identified data attribute related to the prompt, a determined granularity of data for a response to the prompt, and a determined timeframe of analysis for a response to the prompt; and in response to generating the predictive data set, triggering a first automated action based on the predictive data set exceeding the threshold value corresponding to the subset of data associated with the user, wherein the automated action includes adjusting a resource capacity thereby increasing an efficiency of generating additional predictive data sets and key drivers, and wherein the resource capacity is a server capacity or a database capacity. Automatically providing a prompt to an artificial intelligence process is recited broadly and a mental process accomplishable in the human mind or on paper. Automatically generating an analysis of a prompt by an artificial intelligence process is recited broadly and a mental process accomplishable in the human mind or on paper. Examiner notes that applying an artificial intelligence process is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Applying filters to data and generating the predictive data set are each recited generally and are mental processes accomplishable in the human mind or on paper. Identifying an output including a type of the predictive data set is evaluating and a mental process. Triggering an action based on a data set exceeding a threshold is evaluating and a mental process and adjusting a resource capacity of a server or a database is recited broadly and a mental process accomplishable in the human mind or on paper. Each claim recites additional elements of receiving a prompt from a user, which is an input step and insignificant extra-solution activity; and automatically retrieving only a subset of data associated with the user corresponding to the tracking data by applying one or more filters to the tracking data associated with the user based on the analysis of the prompt, wherein the subset of data associated with the user reduces a total amount of data utilized to generate the predictive data set, and retrieving data is also insignificant extra-solution activity. Claim 13 recites a data storage device and a processor and claim 17 recites non-transitory machine-readable medium storing instructions, which are each generic components of a computer system. Examiner notes specification paragraph 0003 describes frustration by a user when looking up information and not knowing how to acquire the most relevant information and paragraphs 0030-0031 describe users being overwhelmed with information and manually looking up sources and not being able to forecast trends based on data. Paragraphs 0032-0033 describes how the invention addresses these challenges by tracking data, analyzing data to predict trends, and providing contextually relevant data with trends to a user. Examiner found support in specification paragraph 0077 for adjusting a resource capacity but this paragraph did not describe how adjusting a resource capacity addresses one of the drawbacks identified in paragraphs 0003 or 0030-0031 as described here. The claim steps still do not recite details of improvements in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, receiving a prompt from a user is recited broadly and amounts to receiving data across a network per specification 0048 and figure 1 140, and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Retrieving a subset of data is retrieving data from memory which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. The data storage device and processor and non-transitory machine-readable medium storing instructions are each still generic components of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claim 2 recites detecting a threshold change for the threshold value in the user relevant data, and detecting a change is evaluating and a mental process; determining a category associated with the threshold change, and determining a category is evaluating and a mental process; determining a key driver based on the threshold change and the category, and determining a key driver is evaluating and a mental process; and in response to determining the key driver, triggering a second automated action based on the key driver, and triggering a second automated action is evaluating and a mental process accomplishable in the human mind or on paper. Claim 3 recites wherein the the artificial intelligence process uses the prompt as an input to provide an output corresponding to the dataset associated with the user, and applying an artificial intelligence process with a prompt is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claims 4, 14, and 18 each recites wherein the identified type of the predictive data set based on the prompt is one of: a time series forecast, and trend analysis, or a key driver analysis. , and requesting data is a mental process accomplishable in the human mind or with pen and paper.
Claims 5 and 15 each recites Claims 5 and 15 each recites storing the generated predictive data set, and storing data is routine and conventional per list of such activities in MPEP 2106.05(d) part II; and determining additional predictive data using the stored generated predictive data set, and determining data is a mental process accomplishable in the human mind or with pen and paper. Claim 6 recites wherein the tracking data is stored in a semantic architecture, and string data is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; wherein the semantic architecture receives the tracking data in a first format or a second format, the first format being different from the second format, and receiving data is recited broadly and amounts to receiving data across a network, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; and wherein the semantic architecture translates the tracking data from the first format or the second format to a third format, wherein the third format is useable to automatically generate the predictive data set, and translating data is recited broadly and is a mental process accomplishable in the human mind or on paper.
Claims 7 and 19 each recites storing the prompt, the analysis of the prompt, and the generated predictive data set in a database with information about past user prompts, and storing data is routine and conventional per list of such activities in MPEP 2106.05(d) part II, wherein generating the identified type of predictive data is further based on the information about past user prompts, and generating data is recited broadly and a mental process accomplishable in the human mind or with pen and paper. Claim 8 recites identifying a subset of data of the user relevant data associated with the prompt, and identifying a subset of data is evaluating and a mental process; and prioritizing the subset of data based on one or more of a user level of access, a user level of interaction, or a frequency of user requests, and prioritizing data is evaluating it and a mental process accomplishable in the human mind or with pen and paper. Claim 9 recites receiving historical user relevant data is recited broadly and amounts to receiving data across a network per specification 0048 and figure 1 140, and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; receiving at least one of contextually-relevant factors or external factors is recited broadly and amounts to receiving data across a network per specification 0048 and figure 1 140, and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; and extracting at least one of a trend, a seasonality, or a residual data from the historical user relevant data, and extracting data is a mental process accomplishable in the human mind or with pen and paper, wherein generating the predictive data set is further based on the contextually-relevant factors, the trend, the seasonality, or the residual data, and generating data is recited broadly and a mental process accomplishable in the human mind or with pen and paper.
Claim 10 recites triggering an automated action based on the generated predictive data set not exceeding the threshold value, and triggering an automated action is an evaluation which is a mental process accomplishable in the human mind or with pen and paper. Claim 11 recites wherein the automated action is performing an additional analysis on the predictive data set, and performing an analysis is recited broadly and a mental process accomplishable in the human mind or with pen and paper. Claims 12, 16, and 20 each recites providing, to the artificial intelligence process, metadata describing the identified metric related to the prompt and metadata describing the identified data attribute related to the prompt, and providing data is a mental process accomplishable in the human mind or with pen and paper.
Relevant Prior Art
During his search for prior art, Examiner found the following references to be relevant to Applicant's claimed invention. Each reference is listed on the Notice of References form included in this office action:
Webster III et al (US 20250291778) teaches techniques for ingesting data and contextualizing it for use, also teaches generating a predictive dataset from a large language model using the ingested data, does not teach a threshold in the ingested data, using tracked user data, filtering tracked user data, sensors, and adjusting resource capacity for a server or a database in response to the predictive dataset (paragraphs 0015-0016, 0026, 0028, 0213-0217 figure 8).
Responses to Applicant’s Remarks
Regarding objections to specification paragraphs 0078-0079 and 0086 for misnumbered components, in view of Applicant filing a replacement specification with corrections in at least these paragraphs, these objections are withdrawn. Regarding objections to claims 1, 13, and 17 for antecedent basis of “the predictive data set requested by the prompt,” in view of amendments reciting “the predictive data set based on the prompt,” these objections are withdrawn. Regarding objections to claims 4, 14, and 18 for antecedent basis of “the identified type of predictive data set requested by the prompt” per the objections in 1, 13, and 17, these objections are also withdrawn per the amendments in claims 1, 13, and 17. Regarding objections to claims 12, 16, and 20 for antecedent basis of “an identified metric related to the prompt” and “an identified data attribute related to the prompt,” in view of amendments reciting each element in the list of elements as included in the output instead of one or more elements, these objections are withdrawn.
Regarding rejections of claims 1-20 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicant’s arguments have been considered but are not persuasive. On pages 15-17 of his Remarks Applicant asserts claim 1 is not directed to a judicial exception. Examiner disagrees and notes the limitations Applicant quotes ("automatically generating, by the artificial intelligence process, an analysis of the prompt, wherein the analysis includes automatically determining a threshold value based on user relevant data corresponding to a dataset associated with the user, wherein the user relevant data includes tracking data identifying one or more data associated with the user including contextually relevant information associated with one or more external factors used to generate a predictive data set," "analyzing the tracking data utilizing the contextually relevant information, wherein the one or more external factors are determined based on a plurality of information received from one or more sensors and the dataset associated with the user," and "identifying an output including an identified type of the predictive data set based on the prompt, an identified metric related to the prompt, an identified data attribute related to the prompt, a determined granularity of data for a response to the prompt, and a determined timeframe of analysis for a response to the prompt.") are identified as mental processes as noted in the rejections above. MPEP 2106.04(a)(2)(III) states "the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation" and "nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer." Examiner notes "automatically retrieving the tracking data and applying one or more filters to the tracking data associated with the user relating to the analysis of the prompt," "automatically generating, in real-time, the predictive data set based on the analysis of the prompt," has been amended to automatically retrieving only a subset of data associated with the user corresponding to the tracking data by applying one or more filters to the tracking data associated with the user based on the analysis of the prompt, wherein the subset of data associated with the user reduces a total amount of data utilized to generate the predictive data set” and is retrieving data from a memory and thus is routine and conventional activity per the rejection above. However, the other actions listed on page 20 ("automatically generating, by the artificial intelligence process, an analysis of the prompt," "automatically generating, in real-time, the predictive data set based on the analysis of the prompt," and "in response to generating the predictive data set, triggering a first automated action based on the predictive data set exceeding the threshold value,") are recited broadly and either apply an artificial intelligence process which is not significantly more than a mental process per the Recentive Analytics decision as shown above or use a computer as a tool (generating a predictive data set, triggering an automated action).
On page 20 Applicant asserts the claim integrates these mental processes into a practical application. Examiner disagrees and notes the two additional elements “receiving a prompt from a user” and “retrieving only a subset of data associated with the user corresponding to the tracking data” are both routine and conventional activities per MPEP 2106.05(d) part II as shown above and do not integrate the mental processes into a practical application per MEP 2106.04(d). Examiner also notes, unlike in Example 47 as Applicant describes on page 23 of his Remarks, the instant claim limitations are recited broadly and do not recite details of the invention that might show an improvement to a technology such as information monitoring or improvements to the drawbacks listed in specification paragraphs 0003 and 0030-0032. For example, the claims do not recite how the invention adjusts a resource capacity or how adjusting a resource capacity increases an efficiency of generating additional predictive data sets. The claim does not recite details on how the artificial intelligence process determines a threshold value or how the invention generates a predictive data set or how the invention applies filters to tracking data to only retrieve a subset, and Examiner does not see how these steps improve upon users being overwhelmed with information when searching (paragraph 0030) or improve upon contextually relevant information fluctuating and losing its applicability (paragraph 0031).
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 5/16/26
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